Evidence (1835 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Inequality
Remove filter
The paper constructs a firm-level measure of AI development using AI-related patent data from Chinese listed firms.
Descriptive/method section: AI-related patent data from Chinese listed firms used to construct a firm-level AI development measure.
Existing literature has extensively examined general AI adoption but limited empirical evidence exists on how more autonomous, agent-like systems contribute to economic outcomes.
Literature review / positioning statement in the introduction of the paper.
The study uses panel data from the World Bank (World Development Indicators and Enterprise Surveys) and OECD AI indicators for the period 2015 to 2024.
Explicit statement of data sources and time period in the paper's methods section.
An AI Adoption Index was constructed using indicators of AI investment, business adoption, and innovation output as a proxy for diffusion of advanced AI capabilities (including agentic features).
Methodological description in the paper: index synthesis from OECD AI indicators and other measures of investment/adoption/innovation; exact index components and weighting described in methods (sample size not applicable).
The review uses a collection of qualitative and quantitative approaches (i.e., it synthesizes both qualitative and quantitative studies).
Explicit methodological description in the abstract indicating mixed-methods literature synthesis.
A collection of qualitative and quantitative approaches reveals predictors of technological integration, including organisational preparedness, economic factors, policies, and human capital.
Statement about the review's synthesized findings from multiple qualitative and quantitative studies identifying these predictors; method = mixed-methods literature synthesis.
The primary technologies covered in this review are Electronic Health Records (EHR), telemedicine, artificial intelligence (AI), and the Internet of Things (IoT).
Explicit topical scope statement in the paper (description of review subjects); based on the paper's own selection of topics for review.
Overall, robot exposure is only weakly related to job-quality outcomes once controls and fixed effects are included.
Individual-level data from the European Working Conditions Telephone Survey (EWCTS) 2021 merged with country–industry robot exposure measures from International Federation of Robotics (IFR) statistics; weighted logistic regression models including individual and job controls and country and industry fixed effects.
Future research should strengthen cross-national comparisons, longitudinal tracking, and interdisciplinary collaboration to support development of a technology governance framework that balances efficiency with equity.
Author recommendation based on identified research gaps in the literature review (prescriptive/recommendation).
Existing research has clear gaps: limited evidence from developing-country contexts, insufficient attention to within-occupation heterogeneity, incomplete accounts of psychological mechanisms underlying AI anxiety, and a shortage of rigorous evaluations of reskilling policy effectiveness.
Author's assessment based on the reviewed literature identifying thematic gaps and methodological limitations (critical literature review).
This review was conducted following the guidelines of the Preferred Reporting of Items in a Systematic Review and Meta-Analysis (PRISMA).
Methodological statement in the paper's abstract indicating PRISMA adherence; no further protocol details or study counts provided in the abstract.
The governance of open-weight artificial intelligence (AI) models has been framed as a binary choice: openness as risk, restriction as safety.
Literature and policy framing review presented in the paper (conceptual/argumentative analysis).
This study proposes a framework for evaluating platform ecosystems by their long-term effects on human capital formation and institutional resilience.
Methodological contribution claimed by the paper (development of an evaluative framework); presented as part of the paper's contributions rather than an empirical finding.
The outreach casenotes used in the study are fairly short and heavily redacted.
Descriptive statement about the dataset of street outreach casenotes provided by the nonprofit partner used in the audit (direct observation by authors).
LLM zero-shot classification does not introduce additional textual biases beyond the algorithmic biases already present in tabular classification.
Authors' assessment/audit comparing zero-shot LLM classification using casenote text against tabular-only classification, concluding no additional textual bias introduced. (Details and sample size not provided in abstract.)
The study's measurement model is supported by Composite Reliability (CR), Average Variance Extracted (AVE), and several model-fit indicators.
Paper explicitly states CR, AVE, and model-fit indices were used and supported the construct measurements and SEM.
Principal Component Analysis (PCA) identified the main constructs related to adoption of FinTech and perceived algorithmic trust.
Paper reports using PCA to identify constructs underlying adoption and perceived algorithmic trust prior to CFA/SEM.
Structured questionnaires were administered to 400 respondents in both city and rural areas of developing countries.
Method section statement specifying a quantitative research design and that structured questionnaires were sent to 400 respondents.
A randomly sampled coalition of equal size remains largely ineffective at increasing platform spending / wages.
Theoretical comparison in the model between targeted coalitions and randomly sampled coalitions of the same size; analytical results showing limited impact for random coalitions.
The article examines the socioeconomic implications of AI-driven automation through the lens of political economy and labor sociology.
Methodological statement in the paper indicating theoretical framing and disciplinary approaches; no empirical sample reported in the abstract.
Technology-driven recruitment encompasses Applicant Tracking Systems (ATS), AI-powered screening, video-based interviews, gamified assessments, and data analytics.
Conceptual description in the paper's introduction/background defining the scope of 'technology-driven recruitment'.
The study employed a mixed-methods research design combining a quantitative survey of 150 HR professionals and recruiters across manufacturing, IT, banking, and education sectors with qualitative case study analysis of four organizations in Chhatrapati Sambhajinagar.
Explicit methodological statement in the paper: quantitative survey (N=150) across specified sectors + qualitative case studies of 4 organizations in Chhatrapati Sambhajinagar.
The review is a focused qualitative evidence synthesis and the proposed governance model is an evidence-informed conceptual framework that warrants future empirical validation.
Authors' explicit framing of the review approach and caveat calling for empirical validation of the proposed model.
Given the focused Title/Abstract/Keywords query and the small, heterogeneous corpus, the findings are interpreted as a scoped evidence map rather than an exhaustive census of all AI-and-work research.
Authors' explicit limitation statement referencing the search strategy (title/abstract/keywords focus), small number of included studies (n=19), and heterogeneity of studies.
Nineteen studies met the eligibility criteria and were analyzed using qualitative thematic synthesis.
Reported result of the screening/eligibility process in the review: final included sample = 19 peer-reviewed articles; analysis method stated as qualitative thematic synthesis.
We conducted a systematic review guided by PRISMA 2020, searching Scopus and Web of Science (Title/Abstract/Keywords) for English-language journal articles published between 2015 and 2025.
Methods reported in the paper: PRISMA 2020-guided systematic review; databases searched explicitly named (Scopus, Web of Science); query fields (Title/Abstract/Keywords); language and date restrictions stated (English, 2015–2025).
The paper analyses the complex interactive relationships among job seekers, recruitment platforms, and enterprises on the basis of the classic theory of incomplete information games.
Methodological description in abstract stating the use of incomplete information game theory to model interactions among stakeholders.
Mainstream recruitment algorithms are taken as the core research object and the multidimensional specific manifestations and internal generation mechanisms of group prejudices in algorithm screening are systematically investigated.
Methodological claim in the paper describing the study's scope and analytic focus (systematic investigation of manifestations and internal mechanisms); no empirical detail provided in abstract.
Existing academic research focuses primarily on macrolevel governance paths of algorithmic discrimination, with relatively insufficient in-depth exploration of the microlevel game logic of job seekers and the construction of systematic adaptation strategies.
Paper's literature review/positioning statement claiming a gap in the literature (macro focus vs. microlevel adaptation under-explored); no systematic literature-mapping statistics provided in abstract.
Today's LLMs are trained to align with user preferences through methods such as reinforcement learning.
Statement of standard practice referenced in the paper, drawing on existing literature about alignment methods (e.g., reinforcement learning from human feedback). This is a descriptive claim about common training techniques rather than an experimental result in this paper.
The review covers publications between 2019 and 2025.
Explicit scope of the literature search reported by the authors (time window of included/considered publications).
The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies.
Synthesis and categorization reported in the paper based on analysis of the included studies (n=18).
The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025.
Explicit methodological reporting in the paper: PRISMA-ScR screening yielded 18 included studies out of 57 candidates over the 2019–2025 period.
This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks.
Authors report conducting a systematic literature survey using a PRISMA-ScR screening process described in the paper.
Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations.
Definition/interpretive claim presented in the paper as conceptual framing (no empirical measurement reported).
Green innovation does not yet significantly reduce carbon inequality.
Empirical results from the provincial panel analysis (2003–2021) showing that measures of green innovation are not associated with a statistically significant reduction in carbon inequality.
Metode penelitian yang digunakan adalah penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif, didukung oleh analisis literatur dari jurnal nasional terindeks SINTA dan jurnal internasional bereputasi.
Pernyataan metode yang jelas tercantum dalam abstrak/metodologi makalah.
Penelitian menilai kecukupan perlindungan hukum yang tersedia bagi pekerja terdampak PHK akibat adopsi AI.
Pernyataan tujuan penelitian dan pendekatan analitis (normatif, komparatif) yang didukung oleh tinjauan literatur pada jurnal-jurnal terpilih.
Penelitian ini bertujuan menganalisis bagaimana Undang-Undang Cipta Kerja dan peraturan turunannya mengklasifikasikan dan menjustifikasi Pemutusan Hubungan Kerja (PHK) akibat adopsi AI.
Pernyataan tujuan penelitian yang tercantum di bagian metodologi/pendahuluan; pendekatan peraturan-perundang-undangan dalam penelitian hukum normatif.
Data construction: The authors treat Wikipedia technology pages as distinct technologies and trace them across patents and job postings from 1976 to 2007, using technical bigrams to identify technologies in texts.
Description of dataset construction building on Kalyani et al. (2025) in Section 2; methodological description of linking Wikipedia pages, patent text, and job postings.
Proposition 1: With a constant pace of technology creation (m(b)=m), the model admits a unique balanced growth path (BGP) along which real wages and output grow at rate g, the skill premium remains constant and is independent of m.
Analytical result (proposition) proved in the paper's model appendix under model assumptions.
The modal technology in the top 1% densest locations (e.g., New York, San Francisco) is 34 years old, while the modal technology in the bottom 50% lowest-density locations is 48 years old, indicating sizable diffusion gaps.
Empirical measurement from the text-based technology dataset tracking vintage of technologies across locations; reported modal ages by location density percentile.
Limitations: the Comscore data observe household internet activity on home (non-mobile) devices and do not capture offline or mobile device activities, so extrapolation to total at-home activities should be done with caution.
Authors' explicit limitation discussion in paper stating data do not include mobile devices or offline activities.
ChatGPT adoption leaves the total time spent on productive online activities (including any time spent using ChatGPT) unchanged.
Same IV long-difference estimates as above; authors state 'leaving time spent on productive digital tasks unchanged' and that total productive activity time does not decline significantly.
The analysis uses detailed Internet browsing microdata from over 200,000 U.S. households' home devices from 2021 to 2024.
Comscore web browsing panel described in paper; authors state dataset covers 'over 200,000 U.S. households' across 2021-2024; data provides timestamps, visit durations, URLs, demographic bins, etc.
AI’s societal integration in India is gradual, and therefore its impact on economic variables (like wages and inequality) is also gradual.
Synthesis in the paper based on empirical adoption figures (e.g., <0.7% adoption for AI ride services) and the observed weak changes in inequality measures in the transportation sector.
Despite AI’s introduction, wage inequality in the transportation sector (measured by the Gini coefficient) has not significantly worsened.
Empirical investigation reported in the paper analyzing transportation-sector wage disparities over time using the Gini coefficient; the paper reports no significant worsening post-introduction.
These energy reductions are achieved without statistically significant performance loss.
Paper states that performance loss is not statistically significant across the evaluated benchmarks (as reported in the abstract).
The study uses a mixed-methods approach combining qualitative insights from 1,500 semi-structured customer interviews with quantitative analysis of transaction records, loan repayment histories, and account activity.
Paper states methods explicitly in abstract: 1,500 semi-structured interviews plus quantitative analysis of transaction records, loan repayment histories, and account activity (case-study approach across three platforms).
This paper uses panel data of China's Shanghai and Shenzhen A-share non-financial listed companies from 2010 to 2022 to study AI's effects.
Explicit data description in the paper (sample frame and period stated).